| Acronym | Name |
|---|---|
| TE | Evergreen trees |
| TDdry | Drought-deciduous trees |
| TDcold | Cold-deciduous trees |
| TN | Needleleaf trees |
| ShE | Evergreen shrubs |
| ShDdry | Drought-deciduous shrubs |
| ShDcold | Cold-deciduous shrubs |
| H | Herbs |
| Geo | Geophytes |
| Thero | Therophytes |
| GC3 | C3 grasses |
| GC4 | C4 grasses |
| Suc | Succulents |
| Clim | Climbers |
Velocity metrics
Estimation of climate velocity (magnitude and direction) for the Phytoclimate of multiple growth forms between the Last Glacial maximum (LGM) and contemporary conditions (1950).
The setup.
Here, I will be exploring how the velocity based novelty metrics in (Ordonez, Williams, and Svenning 2016) can be used in (Conradi et al. 2020) work on Phyto-climates that builds on his work on operationalizing the definition of the biome for global change research.
The novelty metrics in (Ordonez, Williams, and Svenning 2016) focus on measuring three different mechanisms by which ecological novelty might emerge. These mechanisms are based on the idea that as environmental changes happen the composition of taxa on a site will change change.
Our goals are:
Developing new metrics of ecosystem change based on velocity vectors of phytoclimatic change.
Provide a novel and nuanced perspective to identify the ecosystems most at risk from climate change.
Input information.
Here using will use the maps of growth form (GF) suitability for 14 GFs:
Data Inputs - Phytoclimates
All analyses use Phytoclimates as inputs. These variables can be defined as summaries of the climatic suitability for all plant species with a given growth form (e.g. evergreen tree, grass). Its estimation is based on a physiologically informed suitability model (i.e., an Eco-physiological species distribution Model - TTR). The models are based on defining the functional form of physiological constraints to plant growth (as modelled in Dynamic Global Vegetation Models [DGVMs]), parameterizing these using occurrence data, and then using the statistical methods of Species Distribution Modelling (SDMs) to define suitability surfaces for each evaluated species..
The values in the figures above can be defined as the proportion of the species within a growth form for which the environmental conditions at 50x50km a grid-cell are considered suitable at a given period (here 1950; Figure 1).
What is the velocity of phytoclimatic change?.
The Velocity of environmental change idea is built on the approach developed by (Loarie et al. 2009), where velocity for an environmental variable (e.g., temperature) is estimated as:
\[V_{l} = \frac{\text{d}c/\text{d}t}{\text{d}c/\text{d}x}\]
Where \(\frac{\text{d}c}{\text{d}t}\) is the ratio between the projected change per unit time, and \(\frac{\text{d}c}{\text{d}x}\) is the local spatial gradient in the variable of interest.
In this context, velocity vectors for Phytoclimates measure the rapidity and direction of a change in the suitability of environmental conditions for a growth form. You can think of this as a measurement of how fast would the “suitability” surface of a given growth form move in space (like in (Serra-Diaz et al. 2014)).
This metric answer the question, “How fast to move to keep the same ‘suitability’”, assuming that the movement is from an area of low to a place of higher suitability between two times points. A 10km/year value means that to compensate for the change in 1yr, you would need to move to a location 10km away. Here, I apply this approach to each growth form suitability map rather than a single climate variable.
The approach used to estimate the velocity of phytoclimatic change (that is, the magnitude and direction of the change vector) flowing the implementation in (Ordonez et al. 2014) and (Ordonez, Williams, and Svenning 2016).
Maping the velocity of phytoclimatic change.
The magnitude of Phytoclimate velocity vectors (i.e., speed; Figure 2) was the fastest in most of the northern hemisphere, particularly those areas covered by ice sheets during the LGM (the Cordillera, Laurentide, Innuitian, Greenland, Barents-Kara, Fennoscandia and British-Irish Ice Sheets; see (Ehlers, Gibbard, and Hughes 2011)). most likely due to Large temporal changes in suitability.
The Sahara and central and western Australia regions also show large velocities for all GF (Figure 2), particularly drought-deciduous shrubs(ShDdry), drought-deciduous trees (TDdry), Therophytes (Thero), and needle leaf trees (TN)). Such fast speeds are likely due to low suitability for these GF and due to homogeneous spatial patterns.
When comparing the velocity between growth forms in each cell, Climbers (Clim) were the group that showed, in most cases, the fastest speeds for a grid (Figure 3). If Climbers are removed from the analysis, C3 and C4 grasses become the growth forms with the fastest velocities (Figure 4).
Maping the displacement of phytoclimate velocity vectors.
A compound metric of change, that shows the average rapidity in the response across multiple variables is the displacement of velocity vectors (cf. (Ordonez, Williams, and Svenning 2016)). You can think of it as a metric of how fast would you need to move the keep the same environmental setup.
NOTE: Could this be considered how fast would a given GF assemblage (defined by their suitability) need to move to in response to environmental changes?
Using the phytoclimate velocity vectors, the displacement of these is estimated (Figure 5) using the suitability composition for all GF in a location.
This map shows again that the displacement of Phytoclimate velocity vectors (Figure 5) was the fastest in most of the northern hemisphere, particularly those areas covered by ice sheets during the LGM (the Cordillera, Laurentide, Innuitian, Greenland, Barents-Kara, Fennoscandia and British-Irish Ice Sheets; see (Ehlers, Gibbard, and Hughes 2011)).
Is also relevant to consider the balnce between the average and variability in the rapidity of velocity vectors (Figure 6).
References
Conradi, Timo, Jasper A. Slingsby, Guy F. Midgley, Henning Nottebrock, Andreas H. Schweiger, and Steven I. Higgins. 2020. “An Operational Definition of the Biome for Global Change Research.” New Phytol 227 (5): 1294–1306. https://doi.org/10.1111/nph.16580.
Ehlers, Jürgen, Philip L. Gibbard, and Philip D. Hughes, eds. 2011. Quaternary Glaciations - Extent and Chronology: A Closer Look. Developments in Quaternary Science 15. Amsterdam ; Boston: Elsevier.
Loarie, Scott R., Philip B. Duffy, Healy Hamilton, Gregory P. Asner, Christopher B. Field, and David D. Ackerly. 2009. “The Velocity of Climate Change.” Nature 462 (7276): 1052–55. https://doi.org/10.1038/nature08649.
Ordonez, Alejandro, Sebastián Martinuzzi, Volker C. Radeloff, and John W. Williams. 2014. “Combined Speeds of Climate and Land-Use Change of the Conterminous US Until 2050.” Nature Clim Change 4 (9): 811–16. https://doi.org/10.1038/nclimate2337.
Ordonez, Alejandro, John W. Williams, and Jens-Christian Svenning. 2016. “Mapping Climatic Mechanisms Likely to Favour the Emergence of Novel Communities.” Nature Clim Change 6 (12): 1104–9. https://doi.org/10.1038/nclimate3127.
Serra-Diaz, Josep M., Janet Franklin, Miquel Ninyerola, Frank W. Davis, Alexandra D. Syphard, Helen M. Regan, and Makihiko Ikegami. 2014. “Bioclimatic Velocity: The Pace of Species Exposure to Climate Change.” Edited by Matt Fitzpatrick. Diversity Distrib. 20 (2): 169–80. https://doi.org/10.1111/ddi.12131.